Data Engineer

Location: Hybrid in Washington, DC; applicants must reside in the United States at the time of application.

Salary: USD $109,866–$146,488 per year

Company

hirify.global provides AI-powered decision intelligence solutions for national security, supply chain management, and digital identity.

What you will do

• Design and develop end-to-end data pipelines for ingesting, transforming, and delivering structured and unstructured data.
• Build and maintain ETL processes that support data quality, consistency, performance, and scalable processing on AWS.
• Develop schemas, data dictionaries, transformation logic, dataflows, and technical documentation.
• Manage and monitor production datasets, ensuring fault tolerance, redundancy, and data integrity.
• Support database administration, including performance tuning, indexing, access control, and API integration.
• Collaborate with development and cross-functional teams using Agile practices to deliver data features and technical support.

Requirements

• Must possess and maintain a TS-SCI clearance.
• Must reside in the United States at the time of application.
• Bachelor’s degree in computer science, engineering, information systems, or a related field.
• 4–8 years of relevant experience in data engineering, ETL development, or database administration.
• Proficiency with AWS, ETL architecture, data pipeline development, database management, and API development.
• Ability to transform raw data into usable formats and work effectively in Agile development environments.

Nice to have

• Experience with cloud messaging APIs and push notification systems.
• Hands-on database administration and performance tuning experience.
• Experience supporting technical functions related to data infrastructure.
• Familiarity with national security or mission-driven data environments.

Culture & Benefits

• Work on mission-critical applications, analytics, and reporting in complex distributed environments.
• Collaborate across data architecture, engineering, and development teams.
• Apply Agile practices and continuously evaluate new data engineering tools and methods.